1. Field of the Disclosure
The present disclosure relates to systems and methods directed to authentication of payment card transactions. More particularly, the present disclosure relates to systems and methods directed to authentication, verification and validation of payment card user activity as well as for payment card user identity by assessing whether or not an association exists between a user vehicle (e.g., a driverless vehicle or a drone) entering a payment area (e.g., a toll road, a refueling station, or a retail store) and a processing device (e.g., a mobile device such as a cell phone or smart phone) to facilitate determining whether a payment transaction using the processing device is valid or invalid.
2. Description of the Related Art
Payment card companies and merchants incur billions of dollars in losses each year from payment card fraud. Fraud is particularly high in the world of ecommerce where no payment card is actually presented for verification and the actual identity of the user performing the transaction is very difficult, if not impossible, to verify.
Some current fraud mitigation techniques include, for example, merchants requesting a payment card holder's zip code and payment card verification value (CVV) information to verify the identity of the payment card holder. Unfortunately, in a majority of the instances in which the payment card or the payment card number gets compromised, this information also gets compromised, thus reducing the efficacy of these additional pieces of information.
Another technique is for payment card companies to build detailed expensive models on payment card holder behaviors around purchase patterns (things they buy, stores they frequent, etc.) and geolocation movements (places the payment card holder typically travels to). These behaviors are compiled over time and continually evolve. The purchase models are used to detect fraud early and alert merchants and payment card holders. However, the models take months, if not years, to be built for each user and often result in false positives when a user breaks their pattern.
There exists a myriad of current methods that provide for authentication, verification and validation of user activity as well as for user identity. These technologies are used to ensure that an individual is the actual person claimed for the benefit of the activity or transaction. Today, many employed technologies have greatly reduced fraudulent transactions, however instances of fraudulent activity still occur. These technologies are employed, for example, when an individual engages in some transaction that requires some degree of security. An automated financial transaction is a common example of a secure transaction requiring mechanisms to authenticate, verify and validate the identity of the individual attempting to perform the transactional activity. Primary examples of such transactions include performing some banking function, using payment cards (e.g., credit or debit cards) at a point of sale (POS) to make a purchase, that require some form of authentication, verification and validation.
Typical means that are used to authenticate individuals attempting a secure transaction include use of personal identification numbers (PINS) or some other type of information that is assumed to be known only by an authorized user involved in the transaction. Other means of documentation may also be used to verify identity, such as a driver's license or other form of photo identification. Even the use of biometric devices, such as fingerprint scanners, may be used to authenticate an individual attempting to perform a secure transaction. However, even with these and many other technologies employed, fraudulent activity still occurs, and identity theft and misrepresentation remains a problem.
As indicated above, many existing fraud detection and prevention technologies can and do provide a false positive indication of fraudulent activity. Besides the fraud detection and prevention mechanisms already mentioned, other technologies may be employed such as behavioral profiling that is used to detect anomalous behavior. These technologies employ intelligent algorithms to analyze past user behavior when a user attempts to engage in some activity or transaction that is similar to a previous activity or transaction. If the individual's behavior when engaging in a secure activity is not consistent with that individual's past behavior, a likelihood of fraudulent activity may be deduced.
Common examples of this situation are when an individual uses a payment card to purchase some product or service in a foreign country where they have never previously performed a similar transaction. Or, the amount of a particular transaction is significantly different from any previous transaction. This behavior may appear anomalous to a fraud detection system and the activity or transaction being performed may be terminated before any potential fraud is perpetrated. If this is in fact a false positive indication and the individual is actually an authorized user, the user suffers the consequences of a failed transaction and the service provider is perceived to have provided a poor quality of service.
Also, payment cards can be stolen and/or PINS can become compromised and information meant to be held only by authorized users can become known to others. The reality is that other means to perform authentication, verification and validation of authorized users to assist in an authentication process continues to have relevance for transactions where fraudulent activity remains a problem.
With the commercialization of driverless cars, new methods will need to be developed that will provide for authentication, verification and validation of user activity as well as for user identity. Recent legislation has made Nevada the first state where autonomous vehicles can be legally operated on public roads.
For example, driverless cars may include a bunk bed style in which the passengers can sleep horizontally. Passengers can sleep in their driverless cars in the night and wake up at their destination in the morning. In such travel, there is a need for a passive way of making payments for toll charges, refueling, parking, and the like. It would be desirable to have a method that could make the payments without waking the passengers. And with the development of a passive way of making payments, there must also be developed a method for authentication, verification and validation of payment activity as well as for passenger identity.
Thus, there is a need for additional and improved systems and methods to assist, for example, with fraud management systems and identity recognition and authentication. These systems are employed in a variety of industries, including banking and finance, commerce, security and others. In many cases, existing technologies employ detection methods as opposed to prevention methods. That is, many technologies and systems currently in place attempt to detect some fraudulent activity after it has occurred, and then prevent similar fraudulent activity in the future based on this detection. These methods are not optimal as fraudulent activity can be successful in at least one instance prior to detection and subsequent prevention. The prevention of the fraudulent activity at the first attempt to do so, is certainly preferable, as well as reducing incidences of false positive indications of fraud. No present fraud detection and prevention system is perfect. Thus, there is always a need to employ additional technologies to further reduce fraud and identity theft, thereby reducing the economic impact of such undesired activity, especially with the commercialization of new technologies such as driverless cars.